Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910615627366400 |
|---|---|
| author | Batten, Ben Zheng, Yang De Palma, Alessandro Kouvaros, Panagiotis Lomuscio, Alessio |
| author_facet | Batten, Ben Zheng, Yang De Palma, Alessandro Kouvaros, Panagiotis Lomuscio, Alessio |
| contents | We address the problem of verifying neural networks against geometric transformations of the input image, including rotation, scaling, shearing, and translation. The proposed method computes provably sound piecewise linear constraints for the pixel values by using sampling and linear approximations in combination with branch-and-bound Lipschitz optimisation. The method obtains provably tighter over-approximations of the perturbation region than the present state-of-the-art. We report results from experiments on a comprehensive set of verification benchmarks on MNIST and CIFAR10. We show that our proposed implementation resolves up to 32% more verification cases than present approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation Batten, Ben Zheng, Yang De Palma, Alessandro Kouvaros, Panagiotis Lomuscio, Alessio Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition We address the problem of verifying neural networks against geometric transformations of the input image, including rotation, scaling, shearing, and translation. The proposed method computes provably sound piecewise linear constraints for the pixel values by using sampling and linear approximations in combination with branch-and-bound Lipschitz optimisation. The method obtains provably tighter over-approximations of the perturbation region than the present state-of-the-art. We report results from experiments on a comprehensive set of verification benchmarks on MNIST and CIFAR10. We show that our proposed implementation resolves up to 32% more verification cases than present approaches. |
| title | Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13140 |